thesis presentation - rev jd 17july2016

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Development of Scheduling, Path Planning and Resource Management Algorithms for Robotic Fully-automated Multi-story Parking StructureJayanta Kumar Debnath20 July 2016University of Toledo

Electrical Engineering and Computer Science Department

Master of Science in Electrical Engineering(with concentration in Computer Science and Engineering)

Thesis Presentation

IntroductionProblem StatementProposed MethodologyProposed Path Planning AlgorithmProposed Elevator Scheduling AlgorithmProposed Resource Management AlgorithmReal-time Concurrent Simulation ModelSimulation ResultsConclusions and Future Study

Contents

IntroductionWhy Automated Parking?Create spaceIncrease revenueBetter customer securityGreen parking50% less real-estate than traditional parking lot!No drive way roads like traditional parking space!Lower deployment cost plus more revenue generating space!.More security for people and their vehicles!Increase green space and reduce traffic congestion and carbon footprint!Promising and effective solution for busy metropolitan areas parking challenge!

IntroductionCurrent Automated Parking Technology

Stacker Type Automated ParkingTower Type Automated ParkingRequire dedicated lane for stacker crane.Low space utilization.Do not require sophisticated AI Algorithms. Stacker Crane

Require one elevator. Low space utilization.Do not require sophisticated AI Algorithms.Parking capacity is not scalable.

IntroductionCurrent Automated Parking TechnologyChess Type Automated ParkingPuzzle Type Automated ParkingNo dedicated driveway or lane required.Vehicles can be moved horizontally only. Multiple elevators.Maximum space utilization possible.Require sophisticated AI Algorithms.Parking capacity is highly scalable. Each cell require lifting mechanism.Vehicles can be moved both vertically and horizontally.No elevator required.Space utilization less than chess type.Require sophisticated AI Algorithms.Parking capacity is highly scalable.

Puzzle parking structure is similar to chess parking except there is no elevators!

IntroductionCurrent Automated Parking Technology

Moving Vehicles using Roller Bed Mounted Pallets on Chess Type Parking!

Problem StatementMotivation of this Thesis!Robotic and fully automated parking structures are becoming increasingly feasible from the technology perspective.There is a lack of reported designs in literature for a computerized management system for such structures.Artificial Intelligence is well suited for and can enhance efficiency, scalability and mass level commercialization of robotic fully-automated parking structures.

Problem Statement: Design, develop and prototype in simulation an integrated software implementation for a management system that can plan multiple concurrent paths, schedule a group of elevators, and allocate parking space and other related resources in real time with service times acceptable to users.

Problem SpecificationRequirements

Ground Floor Layout (shown for 1020 topology)Vehicle Movement DirectionsNo driving lanes on any floor of the multistory parking structureNumber of parking spaces on a given floor and number of stories are variables.Minimum 80% utilization rate for parking on a given floorNo more than 5 minutes waiting time for delivery or retrieval of a vehicle by driversMultiple independent lifts (or elevators) Robotic carts or pallets move vehicles.Unlimited number of vehicles in motion throughout the structure at any given time Vehicle cart and elevator movements are modeled in compliance with physics.

Proposed MethodologyStorage ProcessStorage Management Algorithm assigns a storage location for a Storage RequestElevator Scheduling Algorithm assigns an elevator and informs customer.Customer leaves their vehicle in the elevator and leave.Elevator transport vehicle to desired floor and unload the vehicle.Path Planning Algorithm finds a path to storage location and moves the vehicle accordingly.

Storage and Retrieval Request Entry Kiosk!Elevator Needed?Select Vehicle Exchange Bay and notify customerCustomer drops off their vehicle in the Vehicle Exchange Bay and leaves.YesNo

Proposed MethodologyRetrieval ProcessStorage Management Algorithm locates vehicle parked at a specific location for a Retrieval Request. Elevator Scheduling Algorithm assigns an elevator and notifies customer.Customer picks up the vehicle and leaves.Elevator transports vehicle to ground floor.Path Planning Algorithm finds a path towards elevator location and moves the vehicle accordingly.

Storage and Retrieval Request Entry Kiosk!Elevator Needed?Select Vehicle Exchange Bay and notify customerPath Planning Algorithm finds a path towards Vehicle Exchange Bay location and moves the vehicle accordingly.YesNo

Proposed MethodologyTheoretical Bounds

What is the minimum number of elevators?What is the minimum number of blank cells?Bounds on minimum number of elevators and blank cells are derivedapplying Queueing Theory on Storage and Retrieval Processes.

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Immovable Obstacle!Movable Obstacle!Starting CellDestination CellDynamic EnvironmentUnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Neighbor Cell on Planned PathUnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Nearest blank cell located using Uniform Cost Search!UnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Neighbor cell on planned path of blank cellSelected blank cellUnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Selected blank cell moved!UnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Vehicle moved!Check for change of immovable obstacle topology!UnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells No change in immovable obstacle topology.Follow previously planned path

Check for change of immovable obstacle topology.

UnblockProcedure


Check for change of immovable obstacle topology.

UnblockProcedure

Proposed Path Planning AlgorithmOverviewD* Lite AlgorithmPath Planning for storage and retrieval of vehicles on cart.Uniform Cost SearchLocating blank cellsD* Lite AlgorithmPath Planning for blank cells Immovable obstacle topology changed!!Re-plan path efficiently which is a special feature of D* Lite algorithm!!

Check for change in immovable obstacle topology.

UnblockProcedure



UnblockProcedure



UnblockProcedure



UnblockProcedure



UnblockProcedure



UnblockProcedure



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UnblockProcedure

Proposed Path Planning AlgorithmD* Lite AlgorithmD* Lite Algorithm is Heuristic Based and Incremental fast re-planning search algorithm and very effective in a dynamic environment.

Uninformed breadth-first searchHeuristic based D* Lite or A* searchHeuristic is good!Grey cells are explored during path planning.

Proposed Path Planning AlgorithmD* Lite AlgorithmD* Lite Algorithm is a Heuristic Based and Incremental fast re-planning search algorithm and very effective in a dynamic environment. A* searchIncremental D* Lite searchIncremental search is able to effectively re-use partial path plan from previous search!

Obstacle added!Obstacle added!Grey cells are explored in re-planning.

Proposed Path Planning AlgorithmUnblock Procedure

Use uniform cost search to locate nearest blank cell

Proposed Path Planning AlgorithmUnblock ProcedureUniform cost search 1st Iteration

No blank cell Found!

Proposed Path Planning AlgorithmUnblock ProcedureUniform cost search 2nd IterationNo blank cell Found!

Proposed Path Planning AlgorithmUnblock ProcedureUniform cost search 3rd IterationNo blank cell found!

Proposed Path Planning AlgorithmUnblock ProcedureUniform cost search 4th IterationBlank cell found!


Two blank cells! Two possible destination cells!Find nearest blank cell destination cell pair.


Nearest blank cell destination cell pair Find nearest blank cell destination cell pair.

Proposed Path Planning AlgorithmUnblock ProcedureUse D* Lite path planning to move blank cell towards destination cell

Proposed Elevator Scheduling AlgorithmTwo-level Integer Programming Formulation Problem Formulation

Each trip will serve one vehicle!HNPGA (Hybrid Nested Partition and Genetic Algorithm) used for High Level Assignment!FIFO used for Low Level Assignment!

Proposed Elevator Scheduling AlgorithmHNPGA : Select Next Most Promising RegionTwo steps at each iteration of HNPGA for selecting next Most Promising Region Step 1: Select best sub region.Step 2: Global verification of selected best sub region with Surrounding Regions.Both steps use Genetic AlgorithmNested Partition Tree!

Proposed Elevator Scheduling AlgorithmStep 1: Select Best Sub RegionTotal 10 vehicles; Each field represents vehicles to schedule Initial populations of GAAfter evaluation cycles!Fittest of final populationsCrossover and MutationBest sub region found by GA! Selected Most Promising Region at first iteration.

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Proposed Elevator Scheduling AlgorithmStep 2: Global VerificationInitial populations of GA uniformly taken from three region.Selected best sub region! Selected Most Promising Region at first iteration.

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Proposed Elevator Scheduling AlgorithmObjective Function of GA

Each field or gene of chromosome represents vehicles to schedule. The value of each field represents the assigned elevator.Total time required to complete storage or retrieval process associated with assigned vehicles.

Proposed Elevator Scheduling AlgorithmCrossover Operator

Proposed Elevator Scheduling AlgorithmMutation Operator

Theoretical Bounds on Resource NeedsQueueing Theory

Server - 01Server - 02Server - S

Mean Arrival Rate, Steady State Condition!M/M/S Queue Model Stochastic Arrival ProcessStochastic Service ProcessMultiple Parallel ServersWithout steady state queue will grow infinitely large eventually.

Proposed Resource Management AlgorithmStatistical ModelsRush hour period customer arrival modelingMorning Rush Hour2 clock hour period from 6:30 AM to 8:30 AM95% of requests are storage5% of requests are retrievalEvening Rush Hour2 clock hour period from 4:00 PM to 6:00 PM95% of requests are retrieval5% of requests are storageInspired by busy downtown business districts traffic pattern

Proposed Resource Management AlgorithmStatistical ModelsPoisson Distributed Customer Arrivals with varying mean arrival rate!

Theoretical Bounds on Resource NeedsBound on Minimum Number of Blank Cells

Modeled part of retrieval process at the beginning of evening rush hour as M/M/S queue where blank cells act as multiple servers to transport vehicles toward elevator load/unload bay!

Theoretical Bounds on Resource NeedsBound on Minimum Number of Blank Cells

Theoretical Bounds on Resource NeedsBound on Minimum Number of Elevators

Modeled part of storage/retrieval process as M/M/S queue where elevators act as multiple servers to transport vehicles between floors!


Starting floorDestination floor DistanceSlow downSpeed upElevator Dynamics


Real-time Concurrent Simulation ModelUnified Modeling Language The overall functionality of simulation is modeled through five major activity modules, which are (a) Automated Parking Lot, (b) Automated Storage Controller, (c) Automated Retrieval Controller, (d) Elevator Controller, and (e) Elevator Scheduler

Modular Simulation Architecture

Real-time Concurrent Simulation ModelUnified Modeling Language State machine diagram for Automated Retrieval Controller: moving towards elevators

Real-time Concurrent Simulation ModelUnified Modeling Language State machine diagram for Automated Storage Controller: moving from elevators

Real-time Concurrent Simulation ModelUnified Modeling Language State machine diagram for Elevator Controller : moving between floors

Real-time Concurrent Simulation ModelUnified Modeling Language Busy-wait Synchronization Techniques used to communicate among concurrent threads.

Timing diagram

Simulation StudyExperimental SetupSpace Utilization > 80% The capacity of parking lot needs to be fully utilized within two clock-hour period43 Test Cases Found!Generating Test CasesNumber of columns on each floor layoutNumber of rows on each floor layoutNumber of floorsMaximum value of mean arrival rate for vehicle requests for the entire parking structure per hour among all rush hour time slots

Simulation StudySimulation SoftwareA software application with multithreading was developed through the Unified Modeling Language (UML) using Java and MATLAB programming languages. Simulation Software was run in Linux environment for better multithreading capability!

Simulation StudyRunning Simulation in 50X Speedup

Simulation StudySimulation ResultsAverage customer waiting time within an impressive 5 minutes mark!

Simulation StudySimulation ResultsIn most cases, average customer waiting time within an impressive 2 minutes mark!

Simulation StudySimulation ResultsFor most cases, the maximum (worst case) customer waiting time is less than 5 minutes although for a small number of cases it was between 10 to 17 minutes!

Simulation StudySimulation ResultsExtreme maximum values occur for very few test cases with 10% immovable carts. In general, maximum waiting times are within the 6-minute mark. Low Probability!

Simulation StudySimulation Results for Case #42 with 10% Immovable CartsMost of the customers experience average waiting times; very few customers have to wait more than the average value!Frequency Distribution of Waiting Times for Individual customers for Case #42Considering 10% immovable cartsConsidering 10% immovable carts

ConclusionsConclusionsIn light of and within the context of the simulation study presented, the design appears feasible for real time deployment in an industrial-grade environment.Average Customer Waiting Time is not more than 5 minutes in most cases! Space Utilization for parking is more than 80% !Design supports customer arrival rates of up to 800 customers per hour!

Future StudyRecommendationsIn the current system, all the parking spots are the same size. Given that there are different size vehicles (sedans, SUVs, mini vans, trucks, etc.) to park, the size of a parking spot would have to match the largest car size. To maximize the available real-estate space utilization rate and enhance the capacity, future studies may consider the design other topologies which may have different size parking spaces.

Study of the effect of robotic cart failures could be extended further to determine the adverse impact on performance more closely.

We assumed, for the analysis based on the queueing theory, that customers would not engage in balking or reneging in the waiting lines. In future studies these and other similar complications can be injected into the statistical models to determine their effects on performance.The research could be extended in the future from the following aspects:

PublicationsDebnath, Jayanta K., and Gursel Serpen. "Real-Time Optimal Scheduling of a Group of Elevators in a Multi-Story Robotic Fully-Automated Parking Structure." Procedia Computer Science 61 (2015): 507-514.

J. Debnath and G. Serpen, Design of Multithreaded Simulation Software through UML for a Fully Automated Robotic Parking Structure, to appear in proceedings of International Conference on Simulation Modeling Practice and Theory, Las Vegas, Nevada, July 2016.

Thank you!Any Questions?

thesis presentation - rev jd 17july2016

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